1. Field of the Invention
The present invention relates to the technical field of image recognition and, more particularly, to a method for aligning gesture features of image.
2. Description of Related Art
In the field of gesture recognition, the vision-based static gesture recognition is made possible by recognizing posture or shape of a gesture image. Hence, techniques about extracting and matching gesture features (e.g., posture or shape) of image are critical with respect to image recognition.
Conventionally, curvature scale space (CSS) descriptors are utilized for obtaining a quantitative shape feature description of the object for recognizing gesture features of image, thereby providing a reliable feature description even though the shape of the object being adversely affected by size, rotation angle, and movement.
The conventional method for gesture recognition first captures an input gesture image, and then a closed curve formed by a binary contour image of the gesture image is determined by preprocessing the gesture image. A CSS image of the gesture image is drawn based on the closed curve. Next, a coordinate with a maximal peak in a coordinate-peak set formed by the CSS image is selected as a basis point for alignment. A circular rotation is performed to generate an aligned CSS image according to the basis point for determining feature parameters of the gesture image. Finally, each feature parameter of the plurality of sets of the gesture image is compared with each feature parameter of a plurality of reference gesture shapes represented as a basis point of the maximal peak, thereby determining a gesture shape corresponding to the gesture image.
However, the first several peaks have about equal values while exhibiting significant curvature changes in the CSS image of gesture shape due to rugged shape of static gesture in which peak may occur at each of three recessed portions between fingers except the thumb. Taking the gesture image with five fingers of a hand representing digit “5” as an example, the maximal peak of the CSS image of the gesture image may occur at the recessed portion between the thumb and index finger, or at the recessed portion between the index and middle fingers. The CSS images represent the same gesture shape no matter where the maximal peak occurs, but the image recognizer may determine different results with the influence of different maximal peaks. Furthermore, the image recognizer may also determine incorrect result when the CSS image is interfered by noise resulted in some other greater peaks. Due to the local limitation, the curvature can only record the “local” curved degree without labeling the size of the whole recessing or protruding area according to the whole contour. Thus, the conventional image recognizer is unreliable and cannot directly determine the position of fingers in the image.